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K-Means clustering is an unsupervised machine learning algorithm widely used in Data Science and Big Data analytics to group similar data points into clusters based on their features. It partitions a dataset into K distinct, non-overlapping clusters by minimizing the within-cluster sum of squares (WCSS), which measures the variance within each cluster. The algorithm works iteratively by initializing K centroids, assigning each data point to the nearest centroid using Euclidean distance, recalculating centroids as the mean of assigned points, and repeating the process until convergence. To apply K-Means clustering in R, a ________ _____ _____ __________ ______ _____ ____ _____ ___ ______ ____ __________.
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